work in this thesis would not have been possible without many people whose contributions are now acknowledged. At Analog Devices Semiconductor: Larry DeVito, Rosamaria Croughwell, and Alex Gusinov, engineers with whom it was a genuine pleasure to work; Bob Surette, for outstanding support in laboratory measurements; Tony Freitas, for considerable layout expertise; Dennis Buss, for all-important financial support; Maryanne Masterson and Frank Holden for fabrication and trim support; Bob Adams, Paul Brokaw, Barrie Gilbert, Janos Kovacs, and Chris Mangelsdorf for enlightening conversations. At Tektronix: Scott Casstevens, for providing the CSA803A for high accuracy jitter measurements; Laszlo Dobos, for his insights into jitter. At Boston University: Anton Mavretic, for his direction and support; David Perreault, Mark Horenstein, and Emile Gergin for their time and effort on the

In recent years, positioning systems for indoor areas using the existing wireless local area network infrastructure have been suggested. Such systems make use of location fingerprinting rather than time or direction of arrival techniques for determining the location of mobile stations. While experimental results related to such positioning systems have been presented, there is a lack of analytical models that can be used as a framework for designing and deploying the positioning systems. In this paper, we present an analytical model for analyzing such positioning systems. We develop the framework for analyzing a simple positioning system that employs the Euclidean distance between a sample signal vector and the location fingerprints of an area stored in a database. We analyze the effect of the number of access points that are visible and radio propagation parameters on the performance of the positioning system and provide some preliminary guidelines on its design.

Abstract—We address the problem of information system survivability, or dynamically preserving intended functionality & computational performance, in the face of malicious intrusive activity. A feedback control approach is proposed which enables tradeoffs between the failure cost of a compromised information system and the maintenance cost of ongoing defensive countermeasures. Online implementation features an inexpensive computation architecture consisting of a sensor-driven recursive estimator followed by an estimate-driven response selector. Offline design features a systematic empirical procedure utilizing a suite of mathematical modeling and numerical optimization tools. The engineering challenge is to generate domain models and decision strategies offline via tractable methods, while achieving online effectiveness. We illustrate the approach with experimentation results for a prototype autonomic defense system which protects its host, a Linux-based web-server, against an automated Internet worm attack. The overall approach applies to other types of computer attacks, network-level security and other domains which could benefit from automatic decision-making based on a sequence of sensor measurements. Index Terms—Computer security, empirical methods, intrusion tolerance, Markovian processes, numerical optimization, sensor uncertainty, stochastic control, survivable systems.

this document. The first section, in chapter 3, develops a model for syntactic dependencies based on word-category n-grams. The second section, in chapter 4, extends this model by allowing short-range word relations to be captured through the incorporation of selected word n-grams.

The performance of automatic speech recognition (ASR) systems degrades greatly when speech is corrupted by noise. Missing feature methods attempt to reduce this degradation by deleting components of a time-frequency representation of speech (such as a spectrogram) that exhibit low signal-to-noise ratio (SNR). Recognition is then performed using only the remaining components of the incomplete spectrogram. These methods have been shown to result in recognition accuracies that are very robust to the effects of additive noise. However, conventional missing feature methods, which modify the classifier used to perform the recognition, suffer from the drawback that they are constrained to use the log-spectral vectors of the spectrogram as features for recognition. It is well known recognition systems that use log-spectral features perform poorly compared to systems that use cepstral features. In this

Motion-compensated video coders typically segment a scene into arbitrary tiles, resulting in a compressed bitstream which is not physically or semantically related to the scene structure. This paper presents a method for segmenting video frames and coding motion of regions, where the regions are defined in terms of a number of different properties. The goal is a video coder which gives good compression while identifying coherent regions in a manner useful for both human users and automated scene-understanding processes. Both a supervised and an unsupervised clustering algorithm are used to segment an image sequence; both algorithms make use of multiple features including motion, texture, position, and color. By utilizing both the structure and motion information, we preserve the semantic/structural content of the different regions, and simultaneously remove the redundancy (in successive frames) by describing the motion information in each region with a six-parameter affine model. In th...

Magnetic resonance (MR) tagging has been shown to be a useful technique for non-invasively measuring the deformation of an in vivo heart. An important step in analyzing tagged images is the identification of tag lines in each image of a cine sequence. Most existing tag identification algorithms require user-defined myocardial contours. Contour identification, however, is time consuming and requires a considerable amount of user intervention. In this paper, a new method for identifying tag lines, which we call the ML/MAP method, is presented that does not require user-defined myocardial contours. The ML/MAP method is composed of three stages. First a set of candidate tag line centers are estimated across the entire region-ofinterest (ROI) with a snake algorithm based on a maximumlikelihood (ML) estimate of the tag center. Next a maximum a posteriori (MAP) hypothesis test is used to detect the candidate tag centers that are actually part of a tag line. Finally a pruning algorithm is used...

This research investigates robust demodulation of Gaussian Minimum Shift Keying (GMSK) signals, using demodulator diversity and real-time bit-error-rate (BER) estimation. GMSK is particularly important because of its use in prominent wireless standards around the world (GSM, DECT, CDPD, DCS1800, and PCS1900). The dissertation begins with a literature review of GMSK demodulation techniques (coherent and noncoherent) and includes an overview of singlechannel interference rejection techniques in digital wireless communications. Various forms of GMSK demodulation are simulated, including the limiter discriminator and di#erential demodulator (i.e., twenty-five variations in all). Ten represent new structures and variations. The demodulator performances are evaluated in realistic wireless environments, such as additive white Gaussian noise, co-channel interference, and multipath environments modeled by COST207 and SMRCIM. Certain

We theoretically formulate the problem of processing continuous-space binary random fields by means of mathematical morphology. This may allow us employ mathematical morphology for developing new statistical techniques for the analysis of binary random images. Since morphological transformations of continuous-space binary random fields are not measurable in general, we are naturally forced to employ intermediate steps that require generation of an equivalent random closed set. The relationship between continuous-space binary random fields and random closed sets is thoroughly investigated. As a by-product of this investigation, a number of useful new results, regarding separability of random closed sets, are presented. Our plan, however, suffers from a few technical problems that are prominent in the continuous case. As an alternative, we suggest morphological discretization of binary random fields, random closed sets, as well as morphological operators, thereby effectively implementing...

Adaptive algorithms for receivers employing antenna arrays have recently received significant attention for radar systems applications. In the majority of these algorithms, the covariance matrix for the clutter-plus-noise is characterized by using samples taken from range cells surrounding the test cell. If the underlying covariance matrix of the test cell is different from the average covariance matrix of the surrounding range cells, significant performance degradation may result. Exact expressions for performance are derived for such cases, when any of a set of popular space-time adaptive processing (STAP) algorithms are used. Numerical evaluation of these expressions illustrates how variations in the parameters of these equations affect probability of detection and probability of false alarm. The equations are utilized to determine an upper bound on the performance of this class of STAP algorithms.